This bachelor thesis elaborates the concept of big data and the classification techniques of machine learning. The focus of this bachelor thesis are the two most common classification-orinted machine learning techniques, k-nearest neighbours and decision tree. Both classification techniques are described, as well as their inputs and the difference between those two classification techniques. A discussion is given on how using the same algorithm can produce different results. Moreover there is a descriptionbed of variable properties and their impact on the possibility of applying a particular classification technique. The process of creating predictive models in R-studio is described as well as problems occurring during the implementation. Finally, a comparison of results obtained by both techiques is given.